OpenRaiser/Intern-Atlas huggingface dataset:
StyleAvatar: Real-time Photo-realistic Portrait Avatar from a Single Video
Venue: arXiv cs.CV 2023
Year: 2023

Face reenactment methods attempt to restore and re-animate portrait videos as realistically as possible. Existing methods face a dilemma in quality versus controllability: 2D GAN-based methods achieve higher image quality but suffer in fine-grained control of facial attributes compared with 3D counterparts. In this work, we propose StyleAvatar, a real-time photo-realistic portrait avatar reconstruction method using StyleGAN-based networks, which can generate high-fidelity portrait avatars with faithful expression control. We expand the capabilities of StyleGAN by introducing a compositional representation and a sliding window augmentation method, which enable faster convergence and improve translation generalization. Specifically, we divide the portrait scenes into three parts for adaptive adjustments: facial region, non-facial foreground region, and the background. Besides, our network leverages the best of UNet, StyleGAN and time coding for video learning, which enables high-quality video generation. Furthermore, a sliding window augmentation method together with a pre-training strategy are proposed to improve translation generalization and training performance, respectively. The proposed network can converge within two hours while ensuring high image quality and a forward rendering time of only 20 milliseconds. Furthermore, we propose a real-time live system, which further pushes research into applications. Results and experiments demonstrate the superiority of our method in terms of image quality, full portrait video generation, and real-time re-animation compared to existing facial reenactment methods. Training and inference code for this paper are at https://github.com/LizhenWangT/StyleAvatar.
Deep Ensembles to Improve Uncertainty Quantification of Statistical Downscaling Models under Climate Change Conditions
Venue: arXiv cs.LG 2023
Year: 2023

Recently, deep learning has emerged as a promising tool for statistical downscaling, the set of methods for generating high-resolution climate fields from coarse low-resolution variables. Nevertheless, their ability to generalize to climate change conditions remains questionable, mainly due to the stationarity assumption. We propose deep ensembles as a simple method to improve the uncertainty quantification of statistical downscaling models. By better capturing uncertainty, statistical downscaling models allow for superior planning against extreme weather events, a source of various negative social and economic impacts. Since no observational future data exists, we rely on a pseudo reality experiment to assess the suitability of deep ensembles for quantifying the uncertainty of climate change projections. Deep ensembles allow for a better risk assessment, highly demanded by sectoral applications to tackle climate change.
Detecting Novelties with Empty Classes
Venue: arXiv cs.CV 2023
Year: 2023

For open world applications, deep neural networks (DNNs) need to be aware of previously unseen data and adaptable to evolving environments. Furthermore, it is desirable to detect and learn novel classes which are not included in the DNNs underlying set of semantic classes in an unsupervised fashion. The method proposed in this article builds upon anomaly detection to retrieve out-of-distribution (OoD) data as candidates for new classes. We thereafter extend the DNN by $k$ empty classes and fine-tune it on the OoD data samples. To this end, we introduce two loss functions, which 1) entice the DNN to assign OoD samples to the empty classes and 2) to minimize the inner-class feature distances between them. Thus, instead of ground truth which contains labels for the different novel classes, the DNN obtains a single OoD label together with a distance matrix, which is computed in advance. We perform several experiments for image classification and semantic segmentation, which demonstrate that a DNN can extend its own semantic space by multiple classes without having access to ground truth.
Physical Adversarial Attacks for Surveillance: A Survey
Venue: arXiv cs.CV 2023
Year: 2023

Modern automated surveillance techniques are heavily reliant on deep learning methods. Despite the superior performance, these learning systems are inherently vulnerable to adversarial attacks - maliciously crafted inputs that are designed to mislead, or trick, models into making incorrect predictions. An adversary can physically change their appearance by wearing adversarial t-shirts, glasses, or hats or by specific behavior, to potentially avoid various forms of detection, tracking and recognition of surveillance systems; and obtain unauthorized access to secure properties and assets. This poses a severe threat to the security and safety of modern surveillance systems. This paper reviews recent attempts and findings in learning and designing physical adversarial attacks for surveillance applications. In particular, we propose a framework to analyze physical adversarial attacks and provide a comprehensive survey of physical adversarial attacks on four key surveillance tasks: detection, identification, tracking, and action recognition under this framework. Furthermore, we review and analyze strategies to defend against the physical adversarial attacks and the methods for evaluating the strengths of the defense. The insights in this paper present an important step in building resilience within surveillance systems to physical adversarial attacks.
An Update-intensive LSM-based R-tree Index
Venue: arXiv cs.DS 2023
Year: 2023

Many applications require update-intensive workloads on spatial objects, e.g., social-network services and shared-riding services that track moving objects. By buffering insert and delete operations in memory, the Log Structured Merge Tree (LSM) has been used widely in various systems because of its ability to handle write-heavy workloads. While the focus on LSM has been on key-value stores and their optimizations, there is a need to study how to efficiently support LSM-based {\em secondary} indexes (e.g., location-based indexes) as modern, heterogeneous data necessitates the use of secondary indexes. In this paper, we investigate the augmentation of a main-memory-based memo structure into an LSM secondary index structure to handle update-intensive workloads efficiently. We conduct this study in the context of an R-tree-based secondary index. In particular, we introduce the LSM RUM-tree that demonstrates the use of an Update Memo in an LSM-based R-tree to enhance the performance of the R-tree's insert, delete, update, and search operations. The LSM RUM-tree introduces new strategies to control the size of the Update Memo to make sure it always fits in memory for high performance. The Update Memo is a light-weight in-memory structure that is suitable for handling update-intensive workloads without introducing significant overhead. Experimental results using real spatial data demonstrate that the LSM RUM-tree achieves up to 9.6x speedup on update operations and up to 2400x speedup on query processing over existing LSM R-tree implementations.
AI & Blockchain as sustainable teaching and learning tools to cope with the 4IR
Venue: arXiv cs.CY 2023
Year: 2023

The Fourth Industrial Revolution (4IR) is transforming the way we live and work, and education is no exception. To cope with the challenges of 4IR, there is a need for innovative and sustainable teaching and learning tools. AI and block chain technologies hold great promise in this regard, with potential benefits such as personalized learning, secure credentialing, and decentralized learning networks. This paper presents a review of existing research on AI and block chain in education, analyzing case studies and exploring the potential benefits and challenges of these technologies. The paper also suggests a unique model for integrating AI and block chain into sustainable teaching and learning practices. Future research directions are discussed, including the need for more empirical studies and the exploration of ethical and social implications. The key summary of this discussion is that, by enhancing accessibility, efficacy, and security in education, AI and blockchain have the potential to revolutionise the field. In order to ensure that students can benefit from these potentially game-changing technologies as technology develops, it will be crucial to find ways to harness its power while minimising hazards. Overall, this paper highlights the potential of AI and block chain as sustainable tools for teaching and learning in the 4IR era and their respective advantages, issues and future prospects have been discussed in this writing.
Computing Expected Motif Counts for Exchangeable Graph Generative Models
Venue: arXiv cs.LG 2023
Year: 2023

Estimating the expected value of a graph statistic is an important inference task for using and learning graph models. This note presents a scalable estimation procedure for expected motif counts, a widely used type of graph statistic. The procedure applies for generative mixture models of the type used in neural and Bayesian approaches to graph data.
IndoorSim-to-OutdoorReal: Learning to Navigate Outdoors without any Outdoor Experience
Venue: arXiv cs.RO 2023
Year: 2023

We present IndoorSim-to-OutdoorReal (I2O), an end-to-end learned visual navigation approach, trained solely in simulated short-range indoor environments, and demonstrates zero-shot sim-to-real transfer to the outdoors for long-range navigation on the Spot robot. Our method uses zero real-world experience (indoor or outdoor), and requires the simulator to model no predominantly-outdoor phenomenon (sloped grounds, sidewalks, etc). The key to I2O transfer is in providing the robot with additional context of the environment (i.e., a satellite map, a rough sketch of a map by a human, etc.) to guide the robot's navigation in the real-world. The provided context-maps do not need to be accurate or complete -- real-world obstacles (e.g., trees, bushes, pedestrians, etc.) are not drawn on the map, and openings are not aligned with where they are in the real-world. Crucially, these inaccurate context-maps provide a hint to the robot about a route to take to the goal. We find that our method that leverages Context-Maps is able to successfully navigate hundreds of meters in novel environments, avoiding novel obstacles on its path, to a distant goal without a single collision or human intervention. In comparison, policies without the additional context fail completely. Lastly, we test the robustness of the Context-Map policy by adding varying degrees of noise to the map in simulation. We find that the Context-Map policy is surprisingly robust to noise in the provided context-map. In the presence of significantly inaccurate maps (corrupted with 50% noise, or entirely blank maps), the policy gracefully regresses to the behavior of a policy with no context. Videos are available at https://www.joannetruong.com/projects/i2o.html
ADVISE: AI-accelerated Design of Evidence Synthesis for Global Development
Venue: arXiv cs.CL 2023
Year: 2023

When designing evidence-based policies and programs, decision-makers must distill key information from a vast and rapidly growing literature base. Identifying relevant literature from raw search results is time and resource intensive, and is often done by manual screening. In this study, we develop an AI agent based on a bidirectional encoder representations from transformers (BERT) model and incorporate it into a human team designing an evidence synthesis product for global development. We explore the effectiveness of the human-AI hybrid team in accelerating the evidence synthesis process. To further improve team efficiency, we enhance the human-AI hybrid team through active learning (AL). Specifically, we explore different sampling strategies, including random sampling, least confidence (LC) sampling, and highest priority (HP) sampling, to study their influence on the collaborative screening process. Results show that incorporating the BERT-based AI agent into the human team can reduce the human screening effort by 68.5% compared to the case of no AI assistance and by 16.8% compared to the case of using a support vector machine (SVM)-based AI agent for identifying 80% of all relevant documents. When we apply the HP sampling strategy for AL, the human screening effort can be reduced even more: by 78.3% for identifying 80% of all relevant documents compared to no AI assistance. We apply the AL-enhanced human-AI hybrid teaming workflow in the design process of three evidence gap maps (EGMs) for USAID and find it to be highly effective. These findings demonstrate how AI can accelerate the development of evidence synthesis products and promote timely evidence-based decision making in global development in a human-AI hybrid teaming context.
Early Classifying Multimodal Sequences
Venue: arXiv cs.LG 2023
Year: 2023

Often pieces of information are received sequentially over time. When did one collect enough such pieces to classify? Trading wait time for decision certainty leads to early classification problems that have recently gained attention as a means of adapting classification to more dynamic environments. However, so far results have been limited to unimodal sequences. In this pilot study, we expand into early classifying multimodal sequences by combining existing methods. We show our new method yields experimental AUC advantages of up to 8.7%.
Evolution of linkages for prototyping of linkage based robots
Venue: arXiv cs.RO 2023
Year: 2023

Prototyping robotic systems is a time consuming process. Computer aided design, however, might speed up the process significantly. Quality-diversity evolutionary approaches optimise for novelty as well as performance, and can be used to generate a repertoire of diverse designs. This design repertoire could be used as a tool to guide a designer and kick-start the rapid prototyping process. This paper explores this idea in the context of mechanical linkage based robots. These robots can be a good test-bed for rapid prototyping, as they can be modified quickly for swift iterations in design. We compare three evolutionary algorithms for optimising 2D mechanical linkages: 1) a standard evolutionary algorithm, 2) the multi-objective algorithm NSGA-II, and 3) the quality-diversity algorithm MAP-Elites. Some of the found linkages are then realized on a physical hexapod robot through a prototyping process, and tested on two different floors. We find that all the tested approaches, except the standard evolutionary algorithm, are capable of finding mechanical linkages that creates a path similar to a specified desired path. However, the quality-diversity approaches that had the length of the linkage as a behaviour descriptor were the most useful when prototyping. This was due to the quality-diversity approaches having a larger variety of similar designs to choose from, and because the search could be constrained by the behaviour descriptors to make linkages that were viable for construction on our hexapod platform.
Deconstructing Student Perceptions of Generative AI (GenAI) through an Expectancy Value Theory (EVT)-based Instrument
Venue: arXiv cs.CY 2023
Year: 2023

This study examines the relationship between student perceptions and their intention to use generative AI in higher education. Drawing on Expectancy-Value Theory (EVT), a questionnaire was developed to measure students' knowledge of generative AI, perceived value, and perceived cost. A sample of 405 students participated in the study, and confirmatory factor analysis was used to validate the constructs. The results indicate a strong positive correlation between perceived value and intention to use generative AI, and a weak negative correlation between perceived cost and intention to use. As we continue to explore the implications of generative AI in education and other domains, it is crucial to carefully consider the potential long-term consequences and the ethical dilemmas that may arise from widespread adoption.
Mitigation of liveness attacks in DAG-based ledgers
Venue: arXiv cs.DC 2023
Year: 2023

The robust construction of the ledger data structure is an essential ingredient for the safe operation of a distributed ledger. While in traditional linear blockchain systems, permission to append to the structure is leader-based, in Directed Acyclic Graph-based ledgers, the writing access can be organised leaderless. However, this leaderless approach relies on fair treatment of non-referenced blocks, i.e. tips, by honest block issuers. We study the impact of a deviation from the standard tip selection by a subset of block issuers with the aim of halting the confirmation of honest blocks entirely. We provide models on this so-called orphanage of blocks and validate these through open-sourced simulation studies. A critical threshold for the adversary issuance rate is shown to exist, above which the tip pool becomes unstable, while for values below the orphanage decrease exponentially. We study the robustness of the protocol with an expiration time on tips, also called garbage collection, and modification of the parent references per block.
Rate-Compatible Polar Codes for Automorphism Ensemble Decoding
Venue: arXiv cs.IT 2023
Year: 2023

Recently, automorphism ensemble decoding (AED) has drawn research interest as a more computationally efficient alternative to successive cancellation list (SCL) decoding of polar codes. Although AED has demonstrated superior performance for specific code parameters, a flexible code design that can accommodate varying code rates does not yet exist. This work proposes a theoretical framework for constructing rate-compatible polar codes with a prescribed automorphism group, which is a key requirement for AED. We first prove that a one-bit granular sequence with useful automorphisms cannot exist. However, by allowing larger steps in the code dimension, flexible code sequences can be constructed. An explicit synthetic channel ranking based on the $β$-expansion is then proposed to ensure that all constructed codes possess the desired symmetries. Simulation results, covering a broad range of code dimensions and blocklengths, show a performance comparable to that of 5G polar codes under cyclic redundancy check (CRC)-aided SCL decoding, however, with lower complexity.
A Survey of Methods for Converting Unstructured Data to CSG Models
Venue: arXiv cs.GR 2023
Year: 2023

The goal of this document is to survey existing methods for recovering CSG representations from unstructured data such as 3D point-clouds or polygon meshes. We review and discuss related topics such as the segmentation and fitting of the input data. We cover techniques from solid modeling and CAD for polyhedron to CSG and B-rep to CSG conversion. We look at approaches coming from program synthesis, evolutionary techniques (such as genetic programming or genetic algorithm), and deep learning methods. Finally, we conclude with a discussion of techniques for the generation of computer programs representing solids (not just CSG models) and higher-level representations (such as, for example, the ones based on sketch and extrusion or feature based operations).
Updatable Learned Indexes Meet Disk-Resident DBMS -- From Evaluations to Design Choices
Venue: arXiv cs.DB 2023
Year: 2023

Although many updatable learned indexes have been proposed in recent years, whether they can outperform traditional approaches on disk remains unknown. In this study, we revisit and implement four state-of-the-art updatable learned indexes on disk, and compare them against the B+-tree under a wide range of settings. Through our evaluation, we make some key observations: 1) Overall, the B+-tree performs well across a range of workload types and datasets. 2) A learned index could outperform B+-tree or other learned indexes on disk for a specific workload. For example, PGM achieves the best performance in write-only workloads while LIPP significantly outperforms others in lookup-only workloads. We further conduct a detailed performance analysis to reveal the strengths and weaknesses of these learned indexes on disk. Moreover, we summarize the observed common shortcomings in five categories and propose four design principles to guide future design of on-disk, updatable learned indexes: (1) reducing the index's tree height, (2) better data structures to lower operation overheads, (3) improving the efficiency of scan operations, and (4) more efficient storage layout.
Dynamic Scheduling for Federated Edge Learning with Streaming Data
Venue: arXiv cs.LG 2023
Year: 2023

In this work, we consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of distributed edge devices with long-term energy constraints. Due to limited communication resources and latency requirements, only a subset of devices is scheduled for participating in the local training process in every iteration. We formulate a stochastic network optimization problem for designing a dynamic scheduling policy that maximizes the time-average data importance from scheduled user sets subject to energy consumption and latency constraints. Our proposed algorithm based on the Lyapunov optimization framework outperforms alternative methods without considering time-varying data importance, especially when the generation of training data shows strong temporal correlation.
Network Error Logging: HTTP Archive Analysis
Venue: arXiv cs.NI 2023
Year: 2023

Network Error Logging helps web server operators detect operational problems in real-time to provide fast and reliable services. HTTP Archive provides detail information of historical data on HTTP requests. This paper leverages the data and provides a long term analysis of Network Error Logging deployment. The deployment raised from 0 to 11.73 % (almost 2,250,000 unique domains) since 2019. Current deployment is dominated by Cloudflare. Although we observed different policies, the default settings prevail. Third party collectors emerge raising the diversity needed to gather sound data. Even so, many service deploy self-hosted services. Moreover, we identify potentially malicious adversaries deploy collectors on randomly-generated domains and shortened URLs.
RT-K-Net: Revisiting K-Net for Real-Time Panoptic Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Panoptic segmentation is one of the most challenging scene parsing tasks, combining the tasks of semantic segmentation and instance segmentation. While much progress has been made, few works focus on the real-time application of panoptic segmentation methods. In this paper, we revisit the recently introduced K-Net architecture. We propose vital changes to the architecture, training, and inference procedure, which massively decrease latency and improve performance. Our resulting RT-K-Net sets a new state-of-the-art performance for real-time panoptic segmentation methods on the Cityscapes dataset and shows promising results on the challenging Mapillary Vistas dataset. On Cityscapes, RT-K-Net reaches 60.2 % PQ with an average inference time of 32 ms for full resolution 1024x2048 pixel images on a single Titan RTX GPU. On Mapillary Vistas, RT-K-Net reaches 33.2 % PQ with an average inference time of 69 ms. Source code is available at https://github.com/markusschoen/RT-K-Net.
Barriers for Social Inclusion in Online Software Engineering Communities -- A Study of Offensive Language Use in Gitter Projects
Venue: arXiv cs.SE 2023
Year: 2023

Social inclusion is a fundamental feature of thriving societies. This paper first investigates barriers for social inclusion in online Software Engineering (SE) communities, by identifying a set of 11 attributes and organising them as a taxonomy. Second, by applying the taxonomy and analysing language used in the comments posted by members in 189 Gitter projects (with > 3 million comments), it presents the evidence for the social exclusion problem. It employs a keyword-based search approach for this purpose. Third, it presents a framework for improving social inclusion in SE communities.